I engineer high-throughput microservices handling billions of requests at Airbnb and build robust, production-grade agentic workflows with RAG and multi-agent frameworks.
I’m Ayush Ranjan, a backend engineer based in San Francisco. I build practical AI and backend systems: robust digital bridges that connect products with millions of users.
I earned my B.Tech in IT from Manipal University Jaipur and completed my master's degree in Computer Science in 2025 at the University of California, Santa Cruz (UCSC), specializing in AI systems and database architecture.
Currently, I'm a Backend Developer at Altimetrik, engineering core billing flows and microservices on the Airbnb Payments Wallet & Instruments team (and previously on the Incentives team). Previously, I led projects at Capgemini for Mercedes-Benz (winning 3rd place at Innocircle 2022) and researched RAG architectures and wearable AI agents in UCSC's academic labs.
At UCSC, I also served as a Teaching Assistant for Database Systems across four terms and Software Engineering for one term, mentoring students in SQL optimization, database design, and practical engineering workflows.
Beyond systems design, I'm a passionate football player, an avid reader of engineering publications, and a cook who treats kitchen experimentation like optimizing distributed pipelines.
Built a Rust CLI proxy that intercepts verbose git command output and compacts it before it reaches an LLM context window - reducing token consumption by up to 74%. Filters git status, diff, log, and more while preserving exit codes for safe automation.
Built a local DOM-aware diff tool for HTML files that visualizes actual rendered changes - not raw code diffs. Features split-view with scroll sync, pure CSS overlays (no DOM injection), a 10-snapshot time-travel history ring, J/K keyboard navigation, and a Chrome MV3 extension for one-keystroke workflow integration.
Designed a code-aware search engine combining semantic (Tree-sitter), structural (SCIP), and literal queries behind FastAPI for LLMs. Created SQLite Librarian and ChromaDB indices with MCP-compatible access for coding agents.
Analyzed image encoding errors in CLIP using the Discrepancy Analysis Framework (DAF) and DINOv2. Discovered 14 systemic faults, including four novel issues. Received an A+ in Neural Computation at UCSC.
Built a containerized pipeline with a JWT authentication gateway, RabbitMQ queues, MongoDB GridFS binary storage, and converter workers orchestrated with Kubernetes in Minikube.
Implemented a multi-filter CNN (sizes 2, 3, 4, 5) in PyTorch to capture n-gram patterns. Tokenized with spaCy and initialized with GloVe embeddings, achieving 87% test accuracy.
Built encoder-decoder LSTM framework with ResNet50 vision extraction. Integrated additive and dot-product attention modules evaluated on validation loss and BLEU metrics.
Built an OpenCV-based face tracking and recognition workflow with a custom Tkinter administration dashboard and Google Text-to-Speech feedback.
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I am open to discussions about scalable backend systems, agentic AI and RAG, research collaborations, or career opportunities. Drop me a line!
San Francisco, CA, USA